def test_homography_warper(self, batch_size, device, dtype):
# generate input data
height, width = 128, 64
eye_size = 3 # identity 3x3
patch_src = torch.ones(batch_size,
1,
height,
width,
device=device,
dtype=dtype)
# create base homography
dst_homo_src = utils.create_eye_batch(batch_size,
eye_size,
device=device,
dtype=dtype)
# instantiate warper
warper = kornia.geometry.transform.HomographyWarper(height,
width,
align_corners=True)
for _ in range(self.num_tests):
# generate homography noise
homo_delta = torch.rand_like(dst_homo_src) * 0.3
dst_homo_src_i = dst_homo_src + homo_delta
# transform the points from dst to ref
patch_dst = warper(patch_src, dst_homo_src_i)
patch_dst_to_src = warper(patch_dst,
_torch_inverse_cast(dst_homo_src_i))
# same transform precomputing the grid
warper.precompute_warp_grid(_torch_inverse_cast(dst_homo_src_i))
patch_dst_to_src_precomputed = warper(patch_dst)
assert_close(patch_dst_to_src_precomputed,
patch_dst_to_src,
atol=1e-4,
rtol=1e-4)
# projected should be equal as initial
error = utils.compute_patch_error(patch_src, patch_dst_to_src,
height, width)
assert error.item() < self.threshold
# check functional api
patch_dst_to_src_functional = kornia.geometry.transform.homography_warp(
patch_dst,
_torch_inverse_cast(dst_homo_src_i), (height, width),
align_corners=True)
assert_close(patch_dst_to_src,
patch_dst_to_src_functional,
atol=1e-4,
rtol=1e-4)
def test_homography_warper(self, batch_size, device, dtype):
# generate input data
height, width = 128, 64
eye_size = 3 # identity 3x3
# create checkerboard
board = utils.create_checkerboard(height, width, 4)
patch_src = torch.from_numpy(board).to(
device=device,
dtype=dtype).view(1, 1, height,
width).expand(batch_size, 1, height, width)
# create base homography
dst_homo_src = utils.create_eye_batch(batch_size,
eye_size,
device=device,
dtype=dtype)
# instantiate warper
warper = kornia.HomographyWarper(height, width, align_corners=True)
for i in range(self.num_tests):
# generate homography noise
homo_delta = torch.rand_like(dst_homo_src) * 0.3
dst_homo_src_i = dst_homo_src + homo_delta
# transform the points from dst to ref
patch_dst = warper(patch_src, dst_homo_src_i)
patch_dst_to_src = warper(patch_dst, torch.inverse(dst_homo_src_i))
# same transform precomputing the grid
warper.precompute_warp_grid(torch.inverse(dst_homo_src_i))
patch_dst_to_src_precomputed = warper(patch_dst)
assert (patch_dst_to_src_precomputed == patch_dst_to_src).all()
# projected should be equal as initial
error = utils.compute_patch_error(patch_src, patch_dst_to_src,
height, width)
assert error.item() < self.threshold
# check functional api
patch_dst_to_src_functional = kornia.homography_warp(
patch_dst,
torch.inverse(dst_homo_src_i), (height, width),
align_corners=True)
assert_allclose(patch_dst_to_src,
patch_dst_to_src_functional,
atol=1e-4,
rtol=1e-4)
def test_homography_warper(self, batch_size, device_type):
# generate input data
height, width = 128, 64
eye_size = 3 # identity 3x3
device = torch.device(device_type)
# create checkerboard
board = utils.create_checkerboard(height, width, 4)
patch_src = torch.from_numpy(board).view(1, 1, height, width).expand(
batch_size, 1, height, width)
patch_src = patch_src.to(device)
# create base homography
dst_homo_src = utils.create_eye_batch(batch_size, eye_size).to(device)
# instantiate warper
warper = kornia.HomographyWarper(height, width)
for i in range(self.num_tests):
# generate homography noise
homo_delta = torch.zeros_like(dst_homo_src)
homo_delta[:, -1, -1] = 0.0
dst_homo_src_i = dst_homo_src + homo_delta
# transform the points from dst to ref
patch_dst = warper(patch_src, dst_homo_src_i)
patch_dst_to_src = warper(patch_dst, torch.inverse(dst_homo_src_i))
# projected should be equal as initial
error = utils.compute_patch_error(patch_dst, patch_dst_to_src,
height, width)
assert error.item() < self.threshold
# check functional api
patch_dst_to_src_functional = kornia.homography_warp(
patch_dst, torch.inverse(dst_homo_src_i), (height, width))
assert utils.check_equal_torch(patch_dst_to_src,
patch_dst_to_src_functional)